Method and system for digital image analysis of ear traits

ABSTRACT

A method of evaluating one or more kernels of an ear of maize using digital imagery includes acquiring a digital image of the one or more kernels of the ear of maize, processing the digital image to estimate at least one physical property of the one or more kernels of the ear of maize from the digital image, and evaluating the at least one kernel of maize using the estimate of the at least one physical property of the at least one kernel of maize.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.13/192,291, filed Jul. 27, 2011, which is a Continuation of U.S.application Ser. No. 11/891,776, filed Aug. 13, 2007, each of which ishereby incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

Breeder knowledge and published information suggests that yieldstability of genotypes over varying environments may be positivelyassociated with greater yield stability within plots. Current combineharvester technology provides a plot average yield and does not allowquantification of differences between plants within the plot. Previousto this invention, within plot stability could be qualitatively assessedby plant breeders using visual methods. This subjective method isdifficult to standardize and depends on breeder knowledge and training.Alternatively, ears could be hand harvested, individually shelled,weighed and kernels could be counted. This manual method is so laborintensive as to make it prohibitive for experiments involving largebreeding populations. Improved methods and systems are needed.

BRIEF SUMMARY OF THE INVENTION

A method of evaluating one or more kernels of an ear of maize usingdigital imagery includes acquiring a digital image of the one or morekernels of the ear of maize, processing the digital image to estimate atleast one physical property of the one or more kernels of the ear ofmaize from the digital image, and evaluating the at least one kernel ofmaize using the estimate of the at least one physical property of the atleast one kernel of maize.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 is a block diagram of a system.

FIG. 2 is a block diagram of a system integrated into a combinationpicker-sheller.

FIG. 3 is a digital image of five separate ears of maize.

FIG. 4 is a representation of FIG. 4 after the area of filled kernels ofeach of the ears has been selected.

FIG. 5 is a representation of FIG. 4 after an edge filter and a spectralfilter have been applied.

FIG. 6 is a representation of FIG. 4 after a large spectral filter hasbeen applied.

FIG. 7 is an image of an ear of maize showing a major and a minor axis.

FIG. 8 is a graph illustrating regression of 1500 genotypes for digitalkernels per ear.

FIG. 9 is a graph illustrating regression of 630 individual ears foryield versus digital area of filled kernels.

FIG. 10 is a graph showing proportion of repressed plants.

FIG. 11 is a graphs illustrating relationship between kernels per earunder stress and proportion of repressed plants.

FIG. 12 is a graph illustrating kernels per ear (count versus standarddeviation of length).

FIG. 13 is an image of an ear of maize with a trace drawn along one rowfor use in determining kernel width.

FIG. 14 is a graph illustrating distances between peaks which correspondto kernel width.

FIG. 15 is a graph illustrating kernel size distributions within an ear.

FIG. 16 is a digital image of five ears of maize.

FIG. 17 is a representation of FIG. 16 where a first pallette is used toselect all kernels and output the maximal length.

FIG. 18 is a representation of FIG. 16 where a second palette is used toselect non-aborted kernels and output the maximal length.

FIG. 19 is a flow diagram of a method for evaluating an ear of maizeusing digital imagery.

FIG. 20 is a flow diagram of a method for using image processing inscreening maize to determine the maize more likely to exhibit stresstolerance and/or those less likely to exhibit stress tolerance.

FIG. 21 is a flow diagram of a method of counting kernels on an ear ofmaize.

FIG. 22 is a flow diagram of an alternate method of counting kernels onan ear of maize.

FIG. 23 is an image illustrating method in FIG. 22 of counting kernelson an ear of maize.

FIG. 24A-24L illustrate one example of a process.

FIG. 25 is an illustration of post processing procedure for identifyingcritical kernel ring.

FIG. 26 is a graph of 1500 elite breeding lines grown under stressshowing count of kernels visible in a digital image versus manual countof kernels per ear.

FIG. 27 is a graph of family averages of elite breeding lines grownunder stress showing count of kernels visible in a digital image versusmanual count of kernels per ear.

FIG. 28 is a graph of 1500 elite breeding lines grown under floweringstress showing area of filled kernels versus the yeild.

FIG. 29 is a graph of family averages of elite breeding lines grownunder stress showing area of filled kernels versus the yeild.

FIG. 30 is a graph of 630 commercial hybrids grown under stress showingarea of filled kernels versus yeild.

FIG. 31 is a graph of 630 commercial hybrids grown under stress showingcount of kernels visible in a digital image versus manual count ofkernels per ear.

FIG. 32 is a graph of 287 plots of commercial hybrids grown under stressshowing area of filled kernels versus yeild.

FIG. 33 is a graph of 287 plots of commercial hybrids grown under stressshowing count of kernels visible in a digital image versus manual countof kernels per ear.

FIG. 34 is a graph of 1500 plots of elite breeding lines showingproportion of repressed plants versus the count of kernels per ear.

FIG. 35 is a graph showing family averages of elite breeding linesshowing proportion of repressed plants versus the count of kernels perear.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

This invention was designed to quantify kernel and ear traits in a highthroughput manner with little degradation of data quality. Thistechnique has already provided insight through the measurement of theproportion of repressed plants within a plot. This trait is alreadybeing used to screen breeding populations for stress tolerance. Thistechnique also allows for the direct estimation of various traits, suchas, kernel abortion, kernel set, yield, kernel numbers per ear,within-ear carbon partitioning and screening of populations for diseasetolerance from these same images. Moreover, measures of within plotvariation for the traits listed above are easily produced. To increasethroughput, ear and image collection could also be automated (e.g.incorporated into a picker-sheller combine).

Digital imaging and appropriate image processing allow for highthroughput quantitative measurement of ear component phenotypes ofindividual maize plants. Such measurements have various uses in plantbreeding applications such as, but not limited to, the study of geneticvariation on a plant-to-plant basis. Examples of traits that may bedetermined from imaging include, without limitation, ear color (such asby determining red, blue, and green density), kernel color (such as bydetermining red, blue, and green density), percent damage resulting fromdiseases or insects (such as Fusarium verticilliodes, Diplodia maydis,Ustilago maydis, Agrotis ipsilon, Blissus Leucopterus, Agramyzaparvicorreis), kernel starch content, kernel fill pattern (such asregular, irregular or other characterization), kernel texture, withinplot variation of any of the previously listed traits, linear slope ofkernel distribution, exponential slope of kernel area distribution,critical kernel ring of kernel area distribution, and count of totalkernel rings from kernel area distribution.

Overview

FIG. 1 provides an overview of a system 10. The system 10 has an imageacquisition and processing component 12. In FIG. 1, one or more ears ofmaize 14 are shown. An image sensor 16 is used to collect image datafrom one or more ears of maize 14. The image sensor 16 may be of anynumber of types of image sensors 16 as may be associated with cameras orimaging devices. For example, the image sensor 16 may be a CCD camera,color sensor, ultrasonic sensor, or light beam sensor. The image sensor16 may provide for color imaging, imaging of specific wavelengths oflight or imaging across a wide spectrum. After the image sensor 16 isused to acquire an image, an image processing system 18 is used toprocess one or more images such as by applying one or more imageprocessing algorithms. Examples of types of algorithms include, withoutlimitation, filtering, watershedding, thresholding, edge finding, edgeenhancement, color selection and spectral filtering. The imageprocessing system 18 also provides for extracting data 20 from one ormore images. The data 20 can include kernel count, kernel sizedistribution, proportion of kernels aborted and other information.

The data 20 extracted from one or more images is used by an analysiscomponent 22. The analysis component 22 has a relationship analysiscomponent 24. The relationship analysis component 24 relates the data 20to one or more physically measured properties or characteristics. Forexample, the relationship analysis component can modify a kernel countfrom the half ear visible in the image by using a previously establishedlinear regression that relates a kernel count determined by imaging to akernel count determined by manual counting if doing so will improvekernel count accuracy. The relationship analysis component 24 may alsobe used to calculate statistics that describe the variation betweenplants within a plot.

A diversity analysis component 26 is also shown. The diversity analysiscomponent 26 may provide for marker analysis, genotypic profiles,phenotypic profiles, or other types of analysis. Based on the resultsfrom the diversity analysis component 26, appropriate germplasm 28 isidentified.

Thus, as shown in FIG. 1, data extracted from digital images of ears ofmaize is used in a manner that elucidates ear traits and interplant andintraear variation of ear traits.

FIG. 2 illustrates a system integrated with a combination pickersheller. A picker 30 is used to pick ears of maize in the conventionalmanner. After ears of maize are picked, they are conveyed along aconveyor path 32 to a shelter 34. Any number of forms of conveyance maybe used as may be most appropriate for a particular design ofcombination picker sheller. The conveyor path 32 allows for ears to beimaged prior to the ears being shelled by the sheller 34. The ears areimaged with the image sensor 16. Image storage 36 may be used forstoring the image. Using image storage 36 allows for the imagingprocessing component 18 to be located remotely from the combinationpicker sheller. For example, the image storage 36 may include digitalmedia such as, but not limited to, solid state, magnetic, or opticalmemory for storing representations of digital images. The digital mediamay then be removed from the combination picker sheller and taken to adifferent site for processing. Alternatively, the image processingcomponent 18 may receive the images wirelessly or the image processingcomponent 18 may be located onboard a combination picker shellermachine, such as a self-powered combination picker sheller. The imageprocessing component 18 then process the images so that data 20 may beextracted. An analysis component 22 then analyzes the data 20.

Acquisition of Images

Ears may be hand or machine harvested at maturity and a digital imagemay be taken under controlled lighting conditions. The image may betaken of one or more ears of corn or of one of more kernels separated orattached to the ear. As the image analysis (discussed below) may involvethe use of spectral filters, the use of controlled lighting conditionsallows for simplifying the use of spectral filters and standardizingdata capture. Without controlled lighting conditions, determinations oflighting conditions may be made and additional calibrations may beperformed to assist in providing proper image processing.

To acquire the image, various types of image sensors may be used. Theimage sensors used may include a charge coupled device (CCD) imagesensor, a camera, video camera, color sensor, laser/light beam sensor,ultrasonic sensor, or other type of image sensor. The image sensor mayprovide for color imaging as color imaging may be desirable wherespectral filters are used. The image sensor may provide for imagingacross a spectrum wider than or different from the visible spectrum. Theimage sensor may be configured to image a single ear, multiple ears,individual kernels or multiple kernels in each frame. If analog imagesare directly acquired instead of digital images, then the analog imagesmay be converted to digital images through scanning or other means.Alternatively, the amount of light intercepted as the ear moves througha light field could provide an alternate means of either two or threedimensional data collection.

Image Processing and Data Extraction

Data is automatically extracted for each ear from digital images usingimage processing software. One example of an image processing softwareapplication which may be used is Image Pro Plus (MediaCybernetics,Silver Spring, Md.). Various image processing operations may beperformed or techniques or algorithms applied as will be discussed ingreater detail. Recorded data for each ear may include, withoutlimitation, minor axis length, major axis length, kernel count, earfilled kernel length, ear filled kernel width, ear filled kernel area,ear filled kernel plus cob length, ear filled kernel plus cob width, earfilled kernel plus cob area, ear color, ear roundness, ear colordensity, kernel area, kernel color (such as red, blue, green density),kernel roundness, filled kernel count, kernel length, kernel width,kernel location in image (x,y coordinates), ear length of filledkernels, ear length of filled plus aborted kernels, ear area of filledplus aborted kernels, ear area of aborted kernels, ear area of filledplus aborted kernels, ear area of aborted kernels, area of damageresulting from Fusarium verticilliodes, Diplodia maydis, Ustilagomaydis, Agrotis ipsilon, Blissus Leucopterus, Agramyza parvicorreisand/or other diseases and/or insects, or other information regardingsize, shape, location, color of an ear, kernel, or portion of an ear orkernel.

FIG. 3 is a photograph of a digital image of five separate ears ofmaize. One image processing technique is to select the filled kernelarea for each ear using a predetermined color palette. FIG. 4 is aphotograph after the filled kernel area of each of the five separateears of maize in the digital image have been selected. The filled kernelarea has been shown to be closely related to individual ear yield. Forthe selected region of each ear, a minor axis length, a major axislength, and a filled kernel area may be calculated.

FIG. 5 illustrates that an edge filter and a spectral filter may beapplied to the digital image of five separate ears of maize. The use ofsuch filters enhances the digital image for purposes of imagesegmentation of the kernels of each kernel. As shown in FIG. 5, both theedge kernels shown in yellow have been identified and application of alarge spectral filter results in bright regions for each kernel whichare separated by black space. In FIG. 5, the edge kernels for each earhave been preserved. Such a filter accentuates the center of thekernels, thereby allowing counting of kernels in the image using a colorpalette. In FIG. 6, a color palete is chosen thereby allowing kernels tobe counted and measured. This count has been shown to be closely relatedto physical kernel counts for each ear.

FIG. 7 is an image of an ear of maize showing a major axis and a minoraxis. Data may be automatically extracted from images in batch modeenabling labor free processing of many images each day. To manuallyprocess such numbers of images would be prohibitively expensive and/ortime-consuming.

As previously discussed, examples of data which may be extracted includeminor axis length, major axis length, the size of the filled kernelarea, and kernel count. It is to be appreciated that these items of dataor other items of data may relate to various traits of interest inbreeding. The following table illustrates examples of such data.

Filled kernel Ear # Count Area Major axis Minor axis 1 1 3.72 3.7291.278 2 7 2.854 3.284 1.1 3 5 3.114 3.248 1.218 4 2 3.373 3.543 1.217 52 2.495 3.038 1.053 5 149 0.915 0.4 0.163 4 154 1.275 0.897 0.166 3 1631.219 0.361 0.164 2 144 0.986 0.26 0.117 1 204 1.279 0.401 0.111Data Analysis

The data may be paired with other data so that relationships between thepairs of data may be determined by regression or other statisticaltechniques used to relate sets of variables. It is to be understood thatthe type of relationship present between pairs of data may vary and assuch different mathematical or statistical tools may be applied. It isto be understood also, that instead of relating two sets of data(pairing), multiple sets of data may be related.

A wide variety of information may be obtained through data analysis.Examples of such information include, without limitation, percent tipkernel abortion, number of kernels aborted, kernel size, percent of lossdue to scattergrain, kernels per ear image, kernels per ear, kernelshape, ear shape, ear length, ear width, area of filled kernels, yield,kernel size distribution from base to tip, kernel weight, kernel color,kernel starch content, kernel fill pattern, kernel texture, percent ofrepressed plants within a plot, percent damage resulting from Fusariumverticilliodes, Diplodia maydis, Ustilago maydis, Agrotis ipsilon,Blissus Leucopterus, Agramyza parvicorreis and/or other diseases and/orinsects, and within plot variation of any of the above values.

FIG. 8 is a graph illustrating regression of 1500 elite breeding plotsfor digital kernels per ear. The graph shows the plot average of theautomatically determined number of kernels per ear visible in thedigital image along the x-axis (Digital KPE). The average number ofkernels per ear for each plot for the traditionally counted kernels perear is shown along the y-axis (Traditional KPE). From this information,a linear regression was performed to relate the Digital KPE to theTraditional KPE. In addition, an R² value was determined in order toprovide a measure of the accuracy of the linear regression. Note thatthe R² value is relatively high for linear fit.Traditional KPE−Digital KPEy=3.0249x−61.956,R ²=0.9456Another linear regression was performed for relating the traditionalkernels per ear to the digitally determined area. Again, note that theR² value is relatively high for the linear fit.Traditional KPE−Areay=0.0071x+0.1439,R ²=0.9449Yet another linear regression was performed for relating the traditionalkernels per ear to the maximum major axis length. Again, a relativelyhigh R² value was determined.Traditional KPE−Max major lengthy=0.0072x+0.4643,R ²=0.9244

FIG. 9 is a graph illustrating regression of 630 individual earsrelating the yield of each individual ear in grams to the filled kernelarea of each individual year. A relatively high R² value is associatedwith the quadratic fit.Traditional yield−filled kernel areay=2.3535x ²+17.028x−0.7258,R ²=0.97

The below table provides R² from regressions of 12 elite breeding familyaverages (about 115 points per family) comparing yield to digital filledkernel area and kernels to digital filled kernel area.

R² Yield Kernels per ear KPE 0.96 0.95 Digital KPE 0.96 0.97 Area 0.980.99 Length 0.96 0.99

Although linear regression has been used in the above examples, it is tobe understood that other types of relationships may be more appropriatedepending upon the physical parameters being related and the number ofphysical parameters being related.

These results suggest that digital imaging (filled kernel areaestimation) can replace traditional manual kernel counts, andtraditional yields.

Within-Plot Variability

The data extracted from the images may be used to quantify within-plotvariability. A “plot” is simply an area where multiple plants of similargenetic background are grown. Within-plot variability describesvariations between plants within the plot. Examples of types ofwithin-plot variability measurements include, without limitation,proportion of repressed plants, or the standard error, standarddeviation, relative standard deviation, skew, kurtosis, variance,coefficient of variation, interquartile range, Gini's mean difference orrange of ear traits.

Proportion of repressed plants is found to be one useful measure ofvariability for a set of plants associated with a plot. Proportion ofrepressed plants (PROPREP) is calculated as the number of repressedplants divided by the total number of plants in a plot. FIG. 10 is agraph explaining the origin of the calculation of the proportion ofrepressed plants. Plots of kernels per ear for individual plants exhibittwo classes of plants when the plants are grown under stress. Referringto Entry 8, the first class of plants possesses greater than 160 kernelsper ear. These plants are referred to as non-repressed. Referring toEntry 7, for example, there is a second class of plants that includesears with 0 to 160 kernels per ear. These ears are referred to asrepressed.

FIG. 11 provides graphs illustrating the relationship between kernelsper ear under flowering stress and PROPREP. PROPREP predicts kernels perear under flowering stress across multiple studies, indicating thatdirect selection on this trait will increase yield stability. Thus,determining kernels per ear automatically may be used for breedingpurposes. In particular, chromosomal regions associated with proportionof repressed plants may be identified. Analysis and identification ofthe chromosomal regions enables molecular breeding with associatedmarkers. FIG. 12 is a graph illustrating kernels per ear (count versusthe standard deviation of length). Thus, it should be clear that inaddition to PROPREP, other measures of within-plot variability can bemade. Note that in FIG. 12, low standard deviations are observed atextremes of kernels per ear (i.e. those plots with five uniformly tinyor large ears).

Kernel Distributions

Kernel distributions within ears have also been measured. FIG. 13 is animage of an ear of maize with a trace drawn along one row for use indetermining kernel size. A user draws trace along one row and an imagingprogram such as Image Pro can be used to automatically identify “peaks”between kernels. FIG. 14 provides a graph showing that Image Pro is ableto automatically identify the peaks between kernels and measure thedistance between those peaks.

FIG. 15 is a graph from Microsoft Excel illustrating kernel sizedistributions within an ear generated from a trace such as the one shownin FIG. 13. Using this data, kernel size distributions within ears maybe measured. This information may be output to Microsoft Excel oranother spreadsheet program or other application to determine thedistances between “peaks” which corresponds to kernel width.

FIG. 16 is a photographic representation of an image of five ears ofmaize exhibiting aborted kernels at the tip of the ear. The proportionof kernels aborted may be measured by processing such an image.

In FIG. 17, a first palette is used to select all kernels and output themaximal length.

In FIG. 18, a second palette is used to select non-aborted kernels andoutput the maximal length. A determination is also shown as to theproportional length loss due to kernel abortion. For each ear from leftto right, the proportion length loss due to kernel abortion is 0.100645,0.082512, 0.05225. 0.099612, 0.083212.

Methods of Operation

FIG. 19 is a flow diagram of a method of evaluating an ear of maizeusing digital imagery. In step 40, a digital image of an ear of maize isacquired. In step 42, the digital image is processed to determine anestimate of at least one physical property of the ear of maize from thedigital image. The processing can include applying a spectral filter tothe digital image. The step of processing can also include extracting afilled kernel area from the ear of maize using a predetermined colorpalette and then counting the number of kernels on the ear of maizerepresented in the digital image. In step 44, the estimate of the atleast one physical property of the ear of maize is compared to estimatesfrom other ears to provide a comparison. The other ears may be on thesame plant, in the same plot or remotely located plots. In step 46, theear of maize is evaluated relative to the other ears of maize based onthe results of the comparison. The physical properties involved mayinclude, without limitation, yield or kernel count.

FIG. 20 is a flow diagram of a method for using image processing inscreening maize to determine the maize more likely to exhibit stresstolerance and/or the maize less likely to exhibit stress tolerance. Instep 48, images of ears of maize are acquired. In step 50, the images ofthe ears of maize are processed. In step 52, estimates of an earcomponent phenotype associated with stress tolerance for each of theears of maize is extracted. In step 54, the estimate of the earcomponent phenotype from a first subset is related to estimates from asecond subset of the ears of maize. The first subset and the secondsubset may consist of ears of maize grown in a single plot or grown inmultiple plots. In step 56, variation between the first subset and thesecond subset is determined. In step 58, ears with stress tolerancerelative to other ears are selected.

FIG. 21 is a flow diagram of a method of counting kernels on an ear ofmaize. In step 60, a digital image of an ear of maize is acquired. Instep 62, a filled kernel area from the ear of maize is extracted using apredetermined color palette. In step 64, a filter is applied to thedigital image to enhance a center of each kernel on the ear of maize. Instep 66, the number of kernels on the ear of maize represented in thedigital image is counted. In step 68, a user is provided an outputrelated to the number of kernels on the ear. The output may be providedon a display or printed form, or through an effect on an automatedprocess.

FIG. 22 and FIG. 23 illustrate another example of a methodology. In FIG.22, step 70 vertical and horizontal hi-gauss 3D convolution filters areapplied. Next, in step 72 a morphological open filter is applied. Instep 74 a convolution top hat kernels filter is applied. The output atthis point in the method is shown in panel A of FIG. 23. Returning toFIG. 22, in step 76, a watershed 3D filter is applied for tracing singlepixel-wide line between kernels. The resulting image at this point inthe method is shown in panel B of FIG. 23. Returning to FIG. 22, in step78 a count/size function is applied to select the color of the singlepixel-wide line. The result of this step is shown in panel C of FIG. 23.Next in step 80 a mask is made and inverted (as shown at panel D of FIG.23) and the mask is applied to the original image through imageoperations functions. In step 92, the color of kernels is selectedthrough use of the count/size function. FIG. 23, panel E shows thisstep. In step 94, pre-processing parameters are collected for eachkernel with a data collector and the pre-processing parameters areautomatically sent to an application such as Microsoft Excel via dynamicdata exchange (DDE) or appended to a text file. This is shown in FIG.23, image panel F. The method shown in FIGS. 22 and 23 is merely onemethod. The specific filters applied, image operations applied,functions used, and software applications used may vary in numerousways.

Examples of pre-processing parameters include, but are not limited tothose in the below table.

PRE-PROCESSING PARAMETERS Ear filled kernel width Ear filled kernel areaEar filled kernel + cob length Ear filled kernel + cob width Ear filledkernel + cob area Ear color Ear elliptical eccentricity Ear colordensity Kernel area Kernel color (red, blue, green density) Kernelelliptical eccentricity Filled kernel count Aborted kernel count Kernellength Kernel width Kernel location in image (x, y coordinates) Earlength of filled kernels Ear length of filled + aborted kernels Ear areaof filled + aborted kernels Ear area of aborted kernels Area of insector disease evidence

Examples of post-processing traits include, but are not limited to thoseset forth in the following table:

POST-PROCESSING TRAITS Percent tip kernel abortion Number of kernelsaborted Kernel size Percent of loss due to scattergrain Kernels per earimage Kernel shape Ear shape Ear length Ear width Area of filled kernelsKernel size distribution from base to tip 100 Kernel weight Kernel colorPercent of repressed plants within a plot Percent insect or diseasedamage Kernel starch content Kernel fill pattern (regular/irregular)Kernel texture Within plot variation of any of the above traitsThus, the results provided may be used in any number of applications.Such applications, include, without limitation, studying of geneticvariation on a plant-to-plant basis, quantifying plant-to-plantvariability for stress tolerance, quantifying damage resulting fromFusarium verticilliodes, Diplodia maydis, Ustilago maydis, AgrotisIpsilon, Blissus Leucopterus, Agramyza parvicorreis and/or otherdiseases and/or insects, characterizing ear type for direct breeding(ear shape and size, kernel shape and size, kernel texture), clarifyingwithin-ear carbon partitioning through grain size changes from base totip of ear, quantifying tip-kernel abortion, nosing back, scattergrainor abnormal kernel set effects, measuring genotypic response tomicro-environmental variation in the field (proxy for measurement oflarge scale genotype by environment effects), testing for the effects ofintroduced transgenes and/or genetic regions (QTL), and determining thedegree to which progeny of a cross are phenotypically similar to eachparent.

NUM OUTPUT# COUNT CENTRX CENTRY AREASUM AREAAV 1 1 135 35.35246 13.4855532.53858 0.2410265 1 2 182 1706.097 658.8908 92396 507.6703 1 3 334.99506 18.08275 42.1449 14.0483 1 4 1 35.56424 14.30337 49.1250749.12507 1 5 2 35.03009 9.472943 53.31369 26.65684 2 1 172 27.3994512.36879 38.41016 0.2233149 2 2 219 1325.732 611.4419 105996 484 2 3 228.23376 11.20392 49.2149 24.60745 2 4 1 27.63672 13.81572 51.784951.7849 2 5 1 27.56835 13.25037 55.72996 55.72996 3 1 136 11.2152313.79884 33.92744 0.2494665 3 2 171 541.6906 680.0285 91096 532.7251 3 31 11.37113 15.08384 41.4021 41.4021 3 4 1 11.36911 15.10278 44.2510944.25109 3 5 3 11.10934 9.269123 47.76082 15.92027 4 1 148 19.0030715.55257 32.92034 0.2224347 4 2 185 921.41 767.9702 92518 500.0972 4 3 218.349 14.0886 43.79158 21.89579 4 4 1 19.20257 16.85983 45.8563145.85631 4 5 2 18.68104 12.28019 50.83179 25.41589 5 1 152 2.993316.31716 35.48301 0.2334408 5 2 182 145.7958 794.9655 93298 512.6264 5 31 2.93908 17.20154 42.43209 42.43209 5 4 1 2.941012 17.30406 47.3298347.32983 5 5 1 2.951466 16.96155 49.79877 49.79877

NUM OUTPUT# MAXLNGTH AVMAJOR AVMINOR MAXMAJOR MAXMINOR 1 1 0.93751520.6804908 0.4470006 0.9380788 0.6932975 1 2 45.1134 31.06593 20.3913345.14059 33.36165 1 3 13.84423 4.95667 1.444745 13.95419 4.002734 1 413.89831 14.6818 4.300627 14.6818 4.300627 1 5 17.47058 8.5028552.205065 16.70202 4.156119 2 1 0.94635 0.678732 0.4059304 0.97732130.6636778 2 2 45.53845 31.09625 19.20178 47.02894 31.93634 2 3 14.209537.71265 2.258259 14.98868 4.23881 2 4 14.24719 15.30998 4.33828515.30998 4.338285 2 5 16.55908 16.89877 4.233573 16.89877 4.233573 3 11.017845 0.730448 0.422351 1.051235 0.7068508 3 2 48.979 33.0193719.97027 50.58573 34.01385 3 3 12.15361 12.80303 4.218792 12.803034.218792 3 4 12.15609 13.19649 4.310743 13.19649 4.310743 3 5 14.177525.199972 1.669618 14.49537 4.205476 4 1 0.8975479 0.6627097 0.41283740.9318126 0.6561371 4 2 43.19018 31.1147 19.90239 44.83906 31.57348 4 311.89686 6.517767 2.362834 12.36636 4.561096 4 4 11.94049 12.713264.624691 12.71326 4.624691 4 5 14.01496 7.927475 2.694953 14.213094.502914 5 1 0.9384155 0.6656501 0.4447012 0.8889209 0.644022 5 269.07759 31.1538 20.8532 73.15811 30.9905 5 3 12.53186 13.00028 4.27201113.00028 4.272011 5 4 12.55004 13.4672 4.502485 13.4672 4.502485 5 514.05047 14.4321 4.420476 14.4321 4.420476

From information shown in the above table, additional information iscalculated. The below table describes trait names and a description ofthe traits that may be calculated.

Trait Name Description Units Values TKERAB* Percent of ear lengthaffected by Percent (%) 0 to 100 kernel abortion (via photometricanalysis) SCTTER Percent of ear area lost due to Percent (%) 0 to 100scatter grain (via photometric analysis) KERFIL Percent of total eararea with Percent (%) 0 to 100 filled kernels (via photometric analysis)KERARE Average area per kernel (via (cm²) 0 to 1 photometric analysis)KERSHA Average kernel shape {elliptical Unitless 0 to 1 eccentricity ofindividual kernel area; 0 = circular; 1 = strongly oval and elongated}(via photometric analysis) EARSHA Ear shape {elliptical eccentricityUnitless 0 to 1 of ear area; 0 = circular; 1 = strongly oval andelongated} (via photometric analysis) PHTYLD Yield per acre at 15%moisture Bu/ac 0 to 500 PHTKPE Total number of kernels per ear Count 0to 1000 PROPREP Proportion of repressed plants is Proportion 0 to 1measured on plot basis by assigning a proportion to the repressed plantsto total plants in the plots

Note that all of these traits may be measured in a high throughputfashion. In addition, all of these traits may be measured on a per plantbasis in a high throughput fashion.

The following table illustrates how traits may be calculated:

Trait calculations KERFIL = (Area of total ear − Area of filledkernels)/Area of total ear TKERAB = (Length of total ear − Length offilled kernels with cob)/Length of total ear SCTTER = (Area of filledkernels with cob − Area of filled kernels)/Area of filled kernels withcob EARSHA = (1 − (Minor axis of total ear²/Major axis of totalear²))^(1/2) KERSHA = (1 − (Av. kernel minor axis²/Av. kernel majoraxis²))^(1/2) KERARE = Av. kernel area EARLGT = Length of total earKEREAR = 0.0033 * (Total kernel count²) + 1.76 * (Total kernel count) −1.92 YIELD = 0.0003 * (Area of total ear²) + 0.0106 * (Area of totalear)

The following tables illustrate calculations based on experimentalresults.

TKERAB YIELD NUM KERFIL (%) (%) SCTTER (%) (bu/ac) KEREAR (COUNT) 10.790508029 0.204473463 0.142089772 0.979593719 417.772 2 0.8830959150.13961464 0.049628367 1.248309855 527.403 3 0.866863257 0.1425799430.064382369 0.953102425 386.763 4 0.861499861 0.14801826 0.0450260831.039501492 426.355 5 0.852071045 0.106788599 0.103481039 0.989924833417.772

EARLGT KERARE NUM (CM) (CM²) KERSHA EARSHA 1 17.47058 0.24102650.753995388 0.968544786 2 16.55908 0.2233149 0.801442602 0.968109998 314.17752 0.2494665 0.815889278 0.956988617 4 14.01496 0.22243470.782258781 0.948487495 5 14.05047 0.2334408 0.744097976 0.951936734

The table below shows an example of individual kernel output for aparticular ear. For each kernel identified in the ear, an area isdetermined, a location (center-x, center-y) is determined and a boxheight and box width associated with the kernel is provided.

INDIVIDUAL KERNEL OUTPUT FOR EAR 5 Box Box Area Center-X Center-Y HeightWidth (Values) (Values) (Values) (Values) (Values) 0.079462 2.7278810.2211 0.311719 0.3948441 0.059165 3.237934 10.20845 0.24937520.4364066 0.057438 2.366096 10.38344 0.311719 0.2909377 0.0652113.613188 10.39283 0.2701565 0.3948441 0.107102 2.697626 10.514310.311719 0.4987504 0.114875 3.672425 10.69321 0.3740628 0.47796920.224568 2.159413 10.98613 0.8104695 0.4987504 0.101919 3.15681510.79569 0.311719 0.5818755 0.16713 2.624615 10.84954 0.39484410.5610942 0.077735 3.656349 10.99086 0.2493752 0.4364066 0.1710173.190554 11.11241 0.4571879 0.5610942 0.053983 4.116021 11.145070.2701565 0.3325003 0.180086 2.562923 11.22776 0.4156253 0.56109420.167994 3.673957 11.31195 0.4156253 0.5818755 0.111852 3.08686111.42889 0.4779692 0.4987504 0.05096 4.12878 11.48288 0.35328150.2909377 0.293666 1.920464 11.66849 0.6857818 0.7065631 0.1334452.578088 11.56225 0.3532815 0.5195317 0.132581 3.627177 11.695040.3948441 0.6234381 0.221113 3.06751 11.79917 0.4779692 0.64421930.068666 4.125016 11.80702 0.311719 0.3532815 0.168426 2.478246 11.899990.3948441 0.5610942 0.240115 3.68105 12.07993 0.5195317 0.64421930.138196 1.961297 12.12275 0.3948441 0.4779692 0.137332 4.17821112.17331 0.4364066 0.4364066 0.264299 3.071858 12.23565 0.49875040.6857818 0.073848 1.588612 12.18268 0.3948441 0.3325003 0.2258632.434587 12.28971 0.4987504 0.6650006 0.068234 4.555175 12.432720.4364066 0.311719 0.198656 3.660078 12.52279 0.4571879 0.60265680.233205 1.829598 12.56747 0.540313 0.5818755 0.172313 1.384897 12.742660.8104695 0.4364066 0.138196 4.208012 12.57435 0.3948441 0.5403130.284597 3.027727 12.69991 0.540313 0.7065631 0.268618 2.375345 12.739210.540313 0.6442193 0.099328 4.670274 12.86324 0.4156253 0.37406280.213339 3.640003 12.92918 0.4779692 0.6234381 0.214635 4.224702 12.990.4987504 0.6234381 0.188723 1.814437 13.0406 0.4571879 0.56109420.226295 1.342057 13.39777 0.9143759 0.4364066 0.283301 3.01876413.17152 0.540313 0.7273444 0.214203 2.359093 13.16937 0.41562530.6234381 0.321737 3.714324 13.39984 0.5610942 0.7273444 0.2116124.333276 13.43814 0.4779692 0.5610942 0.203838 1.76654 13.471190.4987504 0.6234381 0.265163 2.358301 13.58793 0.4779692 0.70656310.271641 3.047347 13.62303 0.5195317 0.7273444 0.209885 3.709713 13.84460.4364066 0.6650006 0.266458 4.33025 13.90701 0.5195317 0.66500060.195633 1.745718 13.89299 0.4779692 0.5818755 0.122217 1.31751713.98579 0.4779692 0.3532815 0.318714 2.365545 14.05303 0.56109420.7273444 0.279414 3.053369 14.07431 0.4987504 0.6857818 0.2949613.686956 14.27323 0.5195317 0.7481257 0.214203 1.710265 14.321470.4779692 0.6026568 0.245729 4.335762 14.381 0.4987504 0.64421930.117466 1.270637 14.46888 0.5195317 0.3740628 0.304462 2.33572614.52012 0.4987504 0.7481257 0.286756 3.03685 14.56676 0.5403130.7065631 0.286756 3.730926 14.73517 0.5195317 0.7896882 0.2301821.68336 14.75817 0.4779692 0.6650006 0.149424 4.341243 14.821610.3740628 0.6442193 0.066938 1.246608 14.85552 0.3325003 0.29093770.294961 2.381174 14.96035 0.4364066 0.8104695 0.096737 3.07766914.91742 0.2285939 0.5195317 0.222409 3.699388 15.12868 0.43640660.7065631 0.255662 1.6843 15.1944 0.4779692 0.6650006 0.261708 4.37843515.21638 0.4571879 0.6857818 0.323896 3.043209 15.29473 0.51953170.7481257 0.268618 2.339129 15.3906 0.4779692 0.7065631 0.2863243.745769 15.55802 0.540313 0.7481257 0.217658 1.67182 15.62067 0.43640660.6857818 0.248752 4.395491 15.66323 0.4779692 0.6857818 0.2845973.03211 15.77229 0.4779692 0.768907 0.284165 2.302552 15.82717 0.49875040.7065631 0.30619 3.722485 16.01429 0.540313 0.7896882 0.267754 1.62720616.05178 0.4987504 0.6857818 0.224136 4.404508 16.1036 0.45718790.6857818 0.277255 2.97389 16.20744 0.4571879 0.7273444 0.2984162.283203 16.29441 0.5195317 0.7065631 0.302303 3.678225 16.465080.4987504 0.768907 0.245297 1.649915 16.50428 0.4364066 0.68578180.231478 4.360732 16.52727 0.4779692 0.6857818 0.288051 2.96841916.64757 0.4571879 0.768907 0.313531 2.292294 16.81439 0.58187550.6442193 0.251343 3.654504 16.91049 0.4571879 0.7065631 0.2560941.662221 16.93386 0.4571879 0.768907 0.266026 4.314239 16.952590.4987504 0.6857818 0.297984 2.953018 17.08801 0.4571879 0.83125070.323464 2.278753 17.41556 0.6026568 0.6857818 0.237092 3.67200117.32499 0.4364066 0.7065631 0.237524 1.659025 17.3442 0.45718790.6857818 0.241842 4.308105 17.3977 0.4571879 0.6650006 0.3070532.982214 17.52746 0.4571879 0.7896882 0.320873 3.676914 17.796290.5818755 0.7481257 0.236228 1.64776 17.77322 0.4779692 0.66500060.263867 4.302879 17.87162 0.5195317 0.6442193 0.349808 2.27390717.97672 0.5610942 0.7273444 0.302303 2.975224 17.98895 0.47796920.7481257 0.256094 1.625249 18.23281 0.4779692 0.6857818 0.2530713.613564 18.26659 0.4779692 0.7273444 0.270345 4.220988 18.400320.5818755 0.6234381 0.251775 2.927628 18.42703 0.4364066 0.72734440.295393 2.247749 18.49347 0.4987504 0.7065631 0.26689 1.606412 18.715850.4987504 0.6650006 0.246593 3.633446 18.68494 0.4571879 0.72734440.297121 2.948403 18.8545 0.4779692 0.7481257 0.169721 4.102952 19.008620.6026568 0.4156253 0.079031 4.379624 19.01361 0.5818755 0.24937520.301871 2.256744 18.97416 0.4987504 0.768907 0.261708 3.600818 19.12610.4779692 0.7273444 0.254366 1.615011 19.20775 0.4987504 0.64421930.329078 2.938204 19.35382 0.5195317 0.7481257 0.332965 2.25311 19.481530.540313 0.768907 0.307485 4.207798 19.5716 0.5818755 0.6650006 0.2798463.586629 19.6036 0.4987504 0.7065631 0.282005 1.627144 19.72957 0.5403130.7273444 0.283733 2.921713 19.85443 0.4779692 0.7273444 0.2858922.266194 19.9934 0.5195317 0.7273444 0.225 4.141775 20.08332 0.51953170.5610942 0.318282 3.552779 20.10986 0.5610942 0.7065631 0.24357 1.6574920.23631 0.4779692 0.6442193 0.361468 2.936688 20.39141 0.58187550.7481257 0.302303 2.299714 20.50508 0.540313 0.7273444 0.3562854.079502 20.64295 0.7273444 0.6857818 0.174904 3.517935 20.614350.4571879 0.4987504 0.305326 1.697186 20.72251 0.5610942 0.78968820.209885 4.618915 20.90527 0.5818755 0.5195317 0.346785 2.993952 20.96330.6234381 0.7273444 0.229318 2.382175 21.01135 0.5195317 0.58187550.289779 3.653756 21.09274 0.5610942 0.7481257 0.209885 1.80493421.20514 0.4156253 0.7273444 0.170153 4.246183 21.26593 0.5403130.4987504 0.104079 4.687254 21.40307 0.4987504 0.3325003 0.1412193.057642 21.39752 0.3532815 0.5610942 0.16929 2.487814 21.437780.3948441 0.5610942 0.386084 3.732702 21.6078 0.5818755 0.8520320.335556 1.802353 21.66194 0.5818755 0.8104695 0.278119 4.36877821.80632 0.6650006 0.6234381 0.333397 3.063003 21.84927 0.62343810.6650006 0.335988 2.45868 21.93161 0.6442193 0.7065631 0.3929943.724163 22.17994 0.6026568 0.852032 0.275527 1.806928 22.228890.6234381 0.7481257 0.183541 4.285489 22.3935 0.5818755 0.58187550.330374 3.027274 22.46846 0.7065631 0.6234381 0.376583 2.43045522.59376 0.7273444 0.7273444 0.358877 3.584681 22.75564 0.64421930.8312507 0.249616 1.921764 22.83297 0.6234381 0.7273444 0.2142034.102499 22.94545 0.6442193 0.6026568 0.112716 1.482317 23.047140.4987504 0.6857818 0.281142 2.714908 23.10937 0.6026568 0.7689070.32951 3.393883 23.25805 0.5610942 0.8728133

Other traits may be determined based on the image processing including,but not limited to ear color (such as red, blue, green density), kernelcolor (such as red, blue, green density), percent damage resulting fromFusarium verticilliodes, Diplodia maydis, Ustilago maydis, AgrotisIpsilon, Blissus Leucopterus, Agramyza parvicorreis and/or otherdiseases and/or insects, kernel starch content, kernel fill pattern(such as regular or irregular), kernel texture, within plot variation ofany of the above traits, linear slope of kernel area distribution,exponential slope of kernel area distribution, critical kernel ring ofkernel area distribution, and count of total kernel rings from kernelarea distribution.

FIG. 24A-24L illustrate one example of a process. FIG. 24A illustratesportions of an original image of an ear taken under controlled lightingconditions. Two views of the original are shown, with the left imagebeing shown at 50 percent size and the right image being shown at 100percent size.

FIG. 24B illustrates results of processing the image shown in FIG. 24A.In FIG. 24B, the background has been removed and replaced with blackcoloration. The purpose of this step is to avoid inadvertently selectingitems in background and including those items in analysis.

FIG. 24C illustrate results of further processing. In FIG. 24C, verticaland horizontal convolution 3D Hi-Gauss filters followed by MorphologicalOpen and Top-Hat filters have been applied. The purpose of the filteringis to increase contrast between adjacent kernels. Of course, otherfiltering techniques may be used.

FIG. 24D illustrate results of further processing. A morphological 3DWatershed filter was applied to yield the results shown. The purpose ofsuch a filter is to separate kernels equally well at base and tip ofear. Of course, other filtering techniques may be used.

FIG. 24E illustrates the creation of a mask from Count/Size selection ofblack coloration that separates kernels. The purpose of such a step isto facilitate application or removal of lines separating kernels.

FIG. 24F illustrates results after further processing. There is an Undoof filters in original image followed by the application of aConvolution 3D low pass filter. The purpose of the step is to identifythe spatial location, length and width of each ear for use in automaticarea of interest (AOI) creation.

FIG. 24G illustrates results after further processing. There is an ImageOperations application of Mask to image followed by automatic creationof AOI for each ear and loading of color palette for colors present inkernels. The purpose of such a step is to output data on per kernel areaand dimensions to calculate KERARE, KERSHA and kernel distributiontraits.

FIG. 24H illustrates results after further processing. There isautomatic creation of AOI to include all ear and loading of colorpalette for colors present in kernels. The purpose of this step is tooutput kernel count for all kernels visible in image to calculateKEREAR.

FIG. 24I illustrates results of further processing. Count/Size filteringon kernel area greater than 450 pixels (or other size) may be performed.The purpose of such a step is to locate division between filled andaborted kernels.

FIG. 24J illustrates results of further processing. An undo of maskapplication is followed by automatic creation of AOI with top set at themaximal y-axis location of filled kernels and selection of color paletteof colors present in kernels. The purpose of such a step is to outputarea of ear section of filled kernels for use in calculation of SCTTER,KERFIL and YIELD.

FIG. 24K illustrates results of further processing. Selection of colorpalette of colors present in kernels and cob is performed. The purposeis to output length of ear section of filled kernels with cob and areaof ear section of filled kernels with cob for use in calculation ofTKERAB and SCTTER.

FIG. 24L illustrates results of further processing. Automatic creationof AOI including the whole ear and selection of color palette of colorspresent in kernels and cob is performed. The purpose of this step is tooutput length, dimensions and area of whole ear for use in calculationof EARLGT, KERFIL, TKERAB, and EARSHA.

FIG. 25 illustrates an example of post-processing procedure forindividual kernel area data. Kernels are assigned to rings (horizontalrows) and a broken stick model is fit to the distribution of medianareas from the base of the ear to the tip. The break point is termed thecritical kernel ring and the slopes before and after the break areoutput as the linear and exponential slopes, respectively.

FIG. 26 is a plot for 1500 plots of elite breeding lines grown underflowering stress (plot averages of 5 ears). The plot shows the count ofkernels visible in a digital image versus the count of kernels per earmanually determined and a calculated linear regression.

FIG. 27 is a plot showing family averages of elite breeding lines grownunder stress. The plot shows the count of kernels visible in a digitalimage versus the count of kernels per ear manually determined and acalculated simple quadratic regression.

FIG. 28 is a plot for 1500 plots of elite breeding lines grown understress (plot averages of 5 ears). The plot shows the area of filledkernels versus the yield as well as a calculated linear regression.

FIG. 29 is a plot showing family averages of elite breeding lines grownunder stress. The plot shows the area of filled kernels versus the yieldas well as a calculated linear regression.

FIG. 30 is a plot for 630 single ear measurements of commercial hybridsgrown under stress. The plot shows the area of filled kernels versus theyield as well as a calculated simple quadratic regression.

FIG. 31 is a plot for 630 single ear measurements of commercial hybridsgrown under stress. The plot shows the count of kernels visible in adigital image versus the count of kernels per ear manually determinedand a calculated simple quadratic regression.

FIG. 32 is a plot for 287 plots of commercial hybrids grown in 3watering treatments (plot averages of 10 ears). The plot shows the areaof filled kernels versus the yield as well as a calculated simplequadratic regression.

FIG. 33 is a plot for 287 plots of commercial hybrids grown in 3watering treatments (plot averages of 10 ears). The plot shows the countof kernels visible in a digital image versus the count of kernels perear manually determined and a calculated linear regression.

FIG. 34 is a plot for 1500 plots of elite breeding lines (plot averagesof 5 ears). The plot shows the proportion of repressed plants versus thecount of kernels per ear as well as a calculated simplequadraticregression.

FIG. 35 is a plot showing family averages of elite breeding lines. Theplot shows the proportion of repressed plants versus the count ofkernels per ear as well as a calculated simple quadratic regression.

The plots shown in FIG. 24 to FIG. 35 and the relationships that maydetermined from the data presented in these plots (such as, but notlimited to regressions) may be incorporated into breeding programs orused in other applications. Such applications, include, withoutlimitation, studying of genetic variation on a plant-to-plant basis,quantifying plant-to-plant variability for stress tolerance, damageresulting from Fusarium verticilliodes, Diplodia maydis, Ustilagomaydis, Agrotis ipsilon, Blissus Leucopterus, Agramyza parvicorreisand/or other diseases and/or insects, characterizing ear type for directbreeding (ear shape and size, kernel shape and size, kernel texture),clarifying within-ear carbon partitioning through grain size changesfrom base to tip of ear, quantifying tip-kernel abortion, nosing back,scattergrain or abnormal kernel set effects, measuring genotypicresponse to micro-environmental variation in the field (proxy formeasurement of large scale genotype by environment effects), testing forthe effects of introduced transgenes and/or genetic regions(quantitative trait loci), and determining the degree to which progenyof a cross are phenotypically similar to each parent. The digitalimaging methods provide for quantitative measurements in a highthroughput fashion so that relevant data may be collected for use inthese and other applications, including breeding, production orevaluation programs.

What is claimed is:
 1. A method of evaluating at least one kernel ofmaize on an ear using digital imagery, comprising steps of: acquiring,by a computing device, a digital image of the at least one kernel ofmaize on an ear, wherein the digital image is taken under controlledlighting conditions; processing, by the computing device, the digitalimage to determine an estimate of at least one physical property of theat least one kernel of maize from the digital image; and evaluating, bythe computing device, the at least one kernel of maize using theestimate of the at least one physical property of the at least onekernel of maize.
 2. The method of claim 1 wherein the physical propertyis filled kernel area.
 3. The method of claim 1 wherein the physicalproperty is kernel size.
 4. The method of claim 1 wherein the physicalproperty is kernel count on at least a portion of an ear of maize. 5.The method of claim 1 wherein the step of processing includes applying aspectral filter to the digital image.
 6. The method of claim 1 whereinthe step of evaluating includes comparing the at least one kernel ofmaize relative to other kernels of maize from a single plot.
 7. Themethod of claim 1 wherein the at least one kernel of maize is from afirst plot and the step of evaluating includes comparing the at leastone kernel of maize relative to other kernels of maize from at least oneadditional plot.
 8. A method of evaluating an ear of maize using digitalimagery, comprising steps of: acquiring, by a computing device, adigital image of the ear of maize, wherein the digital image is takenunder controlled lighting conditions; processing, by the computingdevice, the digital image to determine an estimate of at least onephysical property of the ear of maize from the digital image; comparing,by the computing device, the estimate of at least one physical propertyof the ear of maize to estimates from other ears of maize to provide acomparison; and evaluating, by the computing device, the ear of maizerelative to the other ears of maize based on the comparison.
 9. Themethod of claim 8 wherein the physical property is filled kernel area.10. The method of claim 8 wherein the physical property is kernel count.11. The method of claim 8 wherein the step of processing includesapplying a spectral filter to the digital image.
 12. The method of claim8 wherein the step of processing includes extracting a filled kernelarea from the ear of maize using a predetermined color palette.
 13. Themethod of claim 12 wherein the step of processing further comprisesapplying a filter to the digital image to enhance the center of eachkernel of the ear of maize.
 14. The method of claim 12 furthercomprising counting number of kernels on the ear of maize represented inthe digital image to provide an estimate of at least one physicalproperty.
 15. The method of claim 8 wherein the step of evaluatingincludes comparing the ear of maize relative to the other ears of maizefrom a single plot.
 16. The method of claim 8 wherein ear of maize isfrom a first plot and the step of evaluating includes comparing the earof maize relative to the other ears of maize from at least oneadditional plot.
 17. The method of claim 8 further comprising pickingthe ear of maize prior to the step of acquiring the digital image.